Merge branch 'master' of https://github.com/Itseez/opencv_contrib

doc/tutorials/bioinspired/table_of_content_bioinspired/table_of_content_bioinspired.rst

-.. _Table-Of-Content-Bioinspired:
-
-*bioinspired* module. Algorithms inspired from biological models
-----------------------------------------------------------------
-
-Here you will learn how to use additional modules of OpenCV defined in the "bioinspired" module.
-
- .. include:: ../../definitions/tocDefinitions.rst
-
-+
-  .. tabularcolumns:: m{100pt} m{300pt}
-  .. cssclass:: toctableopencv
-
-  =============== ======================================================
-  |RetinaDemoImg| **Title:** :ref:`Retina_Model`
-
-                  *Compatibility:* > OpenCV 2.4
-
-                  *Author:* |Author_AlexB|
-
-                  You will learn how to process images and video streams with a model of retina filter for details enhancement, spatio-temporal noise removal, luminance correction and spatio-temporal events detection.
-
-  =============== ======================================================
-
-  .. |RetinaDemoImg| image:: images/retina_TreeHdr_small.jpg
-                   :height: 90pt
-                   :width:  90pt
-
- .. raw:: latex
-
-    \pagebreak
-
-.. toctree::
-   :hidden:
-
-   ../retina_model/retina_model

doc/tutorials/cvv/table_of_content_cvv/table_of_content_cvv.rst

-.. _Table-Of-Content-CVV:
-
-*cvv* module. GUI for Interactive Visual Debugging
---------------------------------------------------
-
-Here you will learn how to use the cvv module to ease programming computer vision software through visual debugging aids.
-
- .. include:: ../../definitions/tocDefinitions.rst
-
-+
-  .. tabularcolumns:: m{100pt} m{300pt}
-  .. cssclass:: toctableopencv
-
-  =============== ======================================================
-  |cvvIntro|      *Title:* :ref:`Visual_Debugging_Introduction`
-
-                  *Compatibility:* > OpenCV 2.4.8
-
-                  *Author:* |Author_Bihlmaier|
-
-                  We will learn how to debug our applications in a visual and interactive way.
-
-  =============== ======================================================
-
-  .. |cvvIntro|   image:: images/Visual_Debugging_Introduction_Tutorial_Cover.jpg
-                  :height: 90pt
-                  :width:  90pt
-
-
- .. raw:: latex
-
-    \pagebreak
-
-.. toctree::
-   :hidden:
-
-   ../visual_debugging_introduction/visual_debugging_introduction

doc/tutorials/ximgproc/prediction/prediction.rst

-.. ximgproc:
-
-Structured forests for fast edge detection
-******************************************
-
-Introduction
-------------
-In this tutorial you will learn how to use structured forests for the purpose of edge detection in an image.
-
-Examples
---------
-
-.. image:: images/01.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/02.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/03.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/04.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/05.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/06.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/07.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/08.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/09.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/10.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/11.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-.. image:: images/12.jpg
-  :height: 238pt
-  :width:  750pt
-  :alt: First example
-  :align: center
-
-**Note :** binarization techniques like Canny edge detector are applicable
-           to edges produced by both algorithms (``Sobel`` and ``StructuredEdgeDetection::detectEdges``).
-
-Source Code
------------
-
-.. literalinclude:: ../../../../modules/ximpgroc/samples/cpp/structured_edge_detection.cpp
-   :language: cpp
-   :linenos:
-   :tab-width: 4
-
-Explanation
------------
-
-1. **Load source color image**
-
-  .. code-block:: cpp
-
-    cv::Mat image = cv::imread(inFilename, 1);
-    if ( image.empty() )
-    {
-        printf("Cannot read image file: %s\n", inFilename.c_str());
-        return -1;
-    }
-
-2. **Convert source image to [0;1] range**
-
-  .. code-block:: cpp
-
-    image.convertTo(image, cv::DataType<float>::type, 1/255.0);
-
-3. **Run main algorithm**
-
-  .. code-block:: cpp
-
-    cv::Mat edges(image.size(), image.type());
-
-    cv::Ptr<StructuredEdgeDetection> pDollar =
-        cv::createStructuredEdgeDetection(modelFilename);
-    pDollar->detectEdges(image, edges);
-
-4. **Show results**
-
-  .. code-block:: cpp
-
-    if ( outFilename == "" )
-    {
-        cv::namedWindow("edges", 1);
-        cv::imshow("edges", edges);
-
-        cv::waitKey(0);
-    }
-    else
-        cv::imwrite(outFilename, 255*edges);
-
-Literature
-----------
-For more information, refer to the following papers :
-
-.. [Dollar2013] Dollar P., Zitnick C. L., "Structured forests for fast edge detection",
-                IEEE International Conference on Computer Vision (ICCV), 2013,
-                pp. 1841-1848. `DOI <http://dx.doi.org/10.1109/ICCV.2013.231>`_
-
-.. [Lim2013] Lim J. J., Zitnick C. L., Dollar P., "Sketch Tokens: A Learned
-             Mid-level Representation for Contour and Object Detection",
-             Comoputer Vision and Pattern Recognition (CVPR), 2013,
-             pp. 3158-3165. `DOI <http://dx.doi.org/10.1109/CVPR.2013.406>`_

modules/adas/tools/fcw_detect/CMakeLists.txt

@@ -13,7 +13,7 @@ endif()
 project(${the_target})
 
 ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
-ocv_include_modules(${OPENCV_${the_target}_DEPS})
+ocv_include_modules_recurse(${OPENCV_${the_target}_DEPS})
 
 file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 

modules/adas/tools/fcw_train/CMakeLists.txt

@@ -13,7 +13,7 @@ endif()
 project(${the_target})
 
 ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
-ocv_include_modules(${OPENCV_${the_target}_DEPS})
+ocv_include_modules_recurse(${OPENCV_${the_target}_DEPS})
 
 file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 

modules/bgsegm/include/opencv2/bgsegm.hpp

@@ -57,7 +57,7 @@ namespace bgsegm
 
 /** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
 
-The class implements the algorithm described in @cite KB2001.
+The class implements the algorithm described in @cite KB2001 .
  */
 class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
 {
@@ -86,9 +86,9 @@ means some automatic value.
 CV_EXPORTS_W Ptr<BackgroundSubtractorMOG>
     createBackgroundSubtractorMOG(int history=200, int nmixtures=5,
                                   double backgroundRatio=0.7, double noiseSigma=0);
-                                  
 
-/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012.
+
+/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 .
 
  Takes a series of images and returns a sequence of mask (8UC1)
  images of the same size, where 255 indicates Foreground and 0 represents Background.

modules/bioinspired/doc/bioinspired.rst

-********************************************************************
-bioinspired. Biologically inspired vision models and derivated tools
-********************************************************************
-
-The module provides biological visual systems models (human visual system and others). It also provides derivated objects that take advantage of those bio-inspired models.
-
-.. toctree::
-    :maxdepth: 2
-
-    Human retina documentation <retina>

modules/bioinspired/doc/retina.markdown

-Retina : a Bio mimetic human retina model {#bioinspired_retina}
-=========================================
+Bioinspired Module Retina Introduction {#bioinspired_retina}
+======================================
 
 Retina
 ------
 
-**Note** : do not forget that the retina model is included in the following namespace :
-*cv::bioinspired*.
+@note do not forget that the retina model is included in the following namespace : cv::bioinspired
 
 ### Introduction
 
@@ -18,14 +17,13 @@ separable spatio-temporal filter modelling the two main retina information chann
 
 From a general point of view, this filter whitens the image spectrum and corrects luminance thanks
 to local adaptation. An other important property is its hability to filter out spatio-temporal noise
-while enhancing details. This model originates from Jeanny Herault work @cite Herault2010. It has been
+while enhancing details. This model originates from Jeanny Herault work @cite Herault2010 . It has been
 involved in Alexandre Benoit phd and his current research @cite Benoit2010, @cite Strat2013 (he
 currently maintains this module within OpenCV). It includes the work of other Jeanny's phd student
 such as @cite Chaix2007 and the log polar transformations of Barthelemy Durette described in Jeanny's
 book.
 
-**NOTES :**
-
+@note
 -   For ease of use in computer vision applications, the two retina channels are applied
     homogeneously on all the input images. This does not follow the real retina topology but this
     can still be done using the log sampling capabilities proposed within the class.
@@ -71,7 +69,7 @@ described hereafter. XML parameters file samples are shown at the end of the pag
 
 Here is an overview of the abstract Retina interface, allocate one instance with the *createRetina*
 functions.:
-
+@code{.cpp}
     namespace cv{namespace bioinspired{
 
     class Retina : public Algorithm
@@ -122,6 +120,7 @@ functions.:
       cv::Ptr<Retina> createRetina (Size inputSize);
       cv::Ptr<Retina> createRetina (Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
       }} // cv and bioinspired namespaces end
+@endcode
 
 ### Description
 
@@ -146,59 +145,47 @@ Use : this model can be used basically for spatio-temporal video effects but als
 -   performing motion analysis also taking benefit of the previously cited properties (check out the
     magnocellular retina channel output, by using the provided **getMagno** methods)
 -   general image/video sequence description using either one or both channels. An example of the
-    use of Retina in a Bag of Words approach is given in @cite Strat2013.
+    use of Retina in a Bag of Words approach is given in @cite Strat2013 .
 
 Literature
 ----------
 
 For more information, refer to the following papers :
 
--   Model description :
-
-[Benoit2010] Benoit A., Caplier A., Durette B., Herault, J., "Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI <http://dx.doi.org/10.1016/j.cviu.2010.01.011>
-
--   Model use in a Bag of Words approach :
+-   Model description : @cite Benoit2010
 
-[Strat2013] Strat S., Benoit A., Lambert P., "Retina enhanced SIFT descriptors for video indexing", CBMI2013, Veszprém, Hungary, 2013.
+-   Model use in a Bag of Words approach : @cite Strat2013
 
--   Please have a look at the reference work of Jeanny Herault that you can read in his book :
-
-[Herault2010] Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+-   Please have a look at the reference work of Jeanny Herault that you can read in his book : @cite Herault2010
 
 This retina filter code includes the research contributions of phd/research collegues from which
 code has been redrawn by the author :
 
 -   take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color
-    mosaicing/demosaicing and his reference paper:
-
-[Chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
+    mosaicing/demosaicing and his reference paper: @cite Chaix2007
 
 -   take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which
     originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is
     also proposed and originates from Jeanny's discussions. More informations in the above cited
     Jeanny Heraults's book.
 
--   Meylan&al work on HDR tone mapping that is implemented as a specific method within the model :
-
-[Meylan2007] L. Meylan , D. Alleysson, S. Susstrunk, "A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images", Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
+-   Meylan&al work on HDR tone mapping that is implemented as a specific method within the model : @cite Meylan2007
 
 Demos and experiments !
 -----------------------
 
-**NOTE : Complementary to the following examples, have a look at the Retina tutorial in the
+@note Complementary to the following examples, have a look at the Retina tutorial in the
 tutorial/contrib section for complementary explanations.**
 
 Take a look at the provided C++ examples provided with OpenCV :
 
--  **samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video.
-   Typical uses are (provided your OpenCV installation is situated in folder
-        *OpenCVReleaseFolder*)
-
-        -   image processing : **OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg**
-        -   video processing : **OpenCVReleaseFolder/bin/retinademo -video myMovie.avi**
-        -   webcam processing: **OpenCVReleaseFolder/bin/retinademo -video**
+-   **samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video.
+    Typical uses are (provided your OpenCV installation is situated in folder *OpenCVReleaseFolder*)
+    -   image processing : **OpenCVReleaseFolder/bin/retinademo -image myPicture.jpg**
+    -   video processing : **OpenCVReleaseFolder/bin/retinademo -video myMovie.avi**
+    -   webcam processing: **OpenCVReleaseFolder/bin/retinademo -video**
 
-    **Note :** This demo generates the file *RetinaDefaultParameters.xml* which contains the
+    @note This demo generates the file *RetinaDefaultParameters.xml* which contains the
     default parameters of the retina. Then, rename this as *RetinaSpecificParameters.xml*, adjust
     the parameters the way you want and reload the program to check the effect.
 
@@ -217,7 +204,7 @@ Take a look at the provided C++ examples provided with OpenCV :
     Note that some sliders are made available to allow you to play with luminance compression.
 
     If not using the 'fast' option, then, tone mapping is performed using the full retina model
-    @cite Benoit2010. It includes spectral whitening that allows luminance energy to be reduced.
+    @cite Benoit2010 . It includes spectral whitening that allows luminance energy to be reduced.
     When using the 'fast' option, then, a simpler method is used, it is an adaptation of the
-    algorithm presented in @cite Meylan2007. This method gives also good results and is faster to
+    algorithm presented in @cite Meylan2007 . This method gives also good results and is faster to
     process but it sometimes requires some more parameters adjustement.

modules/ccalib/doc/customPattern.rst

-Custom Calibration Pattern
-==========================
-
-.. highlight:: cpp
-
-CustomPattern
--------------
-A custom pattern class that can be used to calibrate a camera and to further track the translation and rotation of the pattern. Defaultly it uses an ``ORB`` feature detector and a ``BruteForce-Hamming(2)`` descriptor matcher to find the location of the pattern feature points that will subsequently be used for calibration.
-
-.. ocv:class:: CustomPattern : public Algorithm
-
-
-CustomPattern::CustomPattern
-----------------------------
-CustomPattern constructor.
-
-.. ocv:function:: CustomPattern()
-
-
-CustomPattern::create
----------------------
-A method that initializes the class and generates the necessary detectors, extractors and matchers.
-
-.. ocv:function:: bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray())
-
-    :param pattern: The image, which will be used as a pattern. If the desired pattern is part of a bigger image, you can crop it out using image(roi).
-
-    :param boardSize: The size of the pattern in physical dimensions. These will be used to scale the points when the calibration occurs.
-
-    :param output: A matrix that is the same as the input pattern image, but has all the feature points drawn on it.
-
-    :return Returns whether the initialization was successful or not. Possible reason for failure may be that no feature points were detected.
-
-.. seealso::
-
-    :ocv:func:`getFeatureDetector`,
-    :ocv:func:`getDescriptorExtractor`,
-    :ocv:func:`getDescriptorMatcher`
-
-.. note::
-
-   * Determine the number of detected feature points can be done through :ocv:func:`getPatternPoints` method.
-
-   * The feature detector, extractor and matcher cannot be changed after initialization.
-
-
-
-CustomPattern::findPattern
---------------------------
-Finds the pattern in the input image
-
-.. ocv:function:: bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7, const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(), OutputArray H = noArray(), OutputArray pattern_corners = noArray());
-
-    :param image: The input image where the pattern is searched for.
-
-    :param matched_features: A ``vector<Point2f>`` of the projections of calibration pattern points, matched in the image. The points correspond to the ``pattern_points``.``matched_features`` and ``pattern_points`` have the same size.
-
-    :param pattern_points: A ``vector<Point3f>`` of calibration pattern points in the calibration pattern coordinate space.
-
-    :param ratio: A ratio used to threshold matches based on D. Lowe's point ratio test.
-
-    :param proj_error: The maximum projection error that is allowed when the found points are back projected. A lower projection error will be beneficial for eliminating mismatches. Higher values are recommended when the camera lens has greater distortions.
-
-    :param refine_position: Whether to refine the position of the feature points with :ocv:func:`cornerSubPix`.
-
-    :param out: An image showing the matched feature points and a contour around the estimated pattern.
-
-    :param H: The homography transformation matrix between the pattern and the current image.
-
-    :param pattern_corners: A ``vector<Point2f>`` containing the 4 corners of the found pattern.
-
-    :return The method return whether the pattern was found or not.
-
-
-CustomPattern::isInitialized
-----------------------------
-
-.. ocv:function:: bool isInitialized()
-
-    :return If the class is initialized or not.
-
-
-CustomPattern::getPatternPoints
--------------------------------
-
-.. ocv:function:: void getPatternPoints(OutputArray original_points)
-
-    :param original_points: Fills the vector with the points found in the pattern.
-
-
-CustomPattern::getPixelSize
----------------------------
-.. ocv:function:: double getPixelSize()
-
-    :return Get the physical pixel size as initialized by the pattern.
-
-
-CustomPattern::setFeatureDetector
----------------------------------
- .. ocv:function:: bool setFeatureDetector(Ptr<FeatureDetector> featureDetector)
-
-    :param featureDetector: Set a new FeatureDetector.
-
-    :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
-
-.. note::
-
-    * It is left to user discretion to select matching feature detector, extractor and matchers. Please consult the documentation for each to confirm coherence.
-
-
-CustomPattern::setDescriptorExtractor
--------------------------------------
-.. ocv:function:: bool setDescriptorExtractor(Ptr<DescriptorExtractor> extractor)
-
-    :param extractor: Set a new DescriptorExtractor.
-
-    :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
-
-
-CustomPattern::setDescriptorMatcher
------------------------------------
-.. ocv:function:: bool setDescriptorMatcher(Ptr<DescriptorMatcher> matcher)
-
-    :param matcher: Set a new DescriptorMatcher.
-
-    :return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
-
-
-CustomPattern::getFeatureDetector
----------------------------------
-.. ocv:function:: Ptr<FeatureDetector> getFeatureDetector()
-
-    :return The used FeatureDetector.
-
-
-CustomPattern::getDescriptorExtractor
--------------------------------------
-.. ocv:function:: Ptr<DescriptorExtractor> getDescriptorExtractor()
-
-    :return The used DescriptorExtractor.
-
-
-CustomPattern::getDescriptorMatcher
------------------------------------
-.. ocv:function:: Ptr<DescriptorMatcher> getDescriptorMatcher()
-
-    :return The used DescriptorMatcher.
-
-
-CustomPattern::calibrate
-------------------------
-Calibrates the camera.
-
-.. ocv:function:: double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
-
-    See :ocv:func:`calibrateCamera` for parameter information.
-
-
-CustomPattern::findRt
----------------------
-Finds the rotation and translation vectors of the pattern.
-
-.. ocv:function:: bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE)
-.. ocv:function:: bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE)
-
-    :param image: The image, in which the rotation and translation of the pattern will be found.
-
-    See :ocv:func:`solvePnP` for parameter information.
-
-
-CustomPattern::findRtRANSAC
----------------------------
-Finds the rotation and translation vectors of the pattern using RANSAC.
-
-.. ocv:function:: bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE)
-.. ocv:function:: bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE)
-
-    :param image: The image, in which the rotation and translation of the pattern will be found.
-
-    See :ocv:func:`solvePnPRANSAC` for parameter information.
-
-
-CustomPattern::drawOrientation
-------------------------------
-Draws the ``(x,y,z)`` axis on the image, in the center of the pattern, showing the orientation of the pattern.
-
-.. ocv:function:: void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix, InputArray distCoeffs, double axis_length = 3, int axis_width = 2)
-
-    :param image: The image, based on which the rotation and translation was calculated. The axis will be drawn in color - ``x`` - in red, ``y`` - in green, ``z`` - in blue.
-
-    :param tvec: Translation vector.
-
-    :param rvec: Rotation vector.
-
-    :param cameraMatrix: The camera matrix.
-
-    :param distCoeffs: The distortion coefficients.
-
-    :param axis_length: The length of the axis symbol.
-
-    :param axis_width: The width of the axis symbol.
-

modules/cvv/doc/cvv.rst

-*********************************************************************
-cvv. GUI for Interactive Visual Debugging of Computer Vision Programs
-*********************************************************************
-
-The module provides an interactive GUI to debug and incrementally design computer vision algorithms. The debug statements can remain in the code after development and aid in further changes because they have neglectable overhead if the program is compiled in release mode.
-
-.. toctree::
-    :maxdepth: 2
-
-    CVV API Documentation <cvv_api>
-    CVV GUI Documentation <cvv_gui>

modules/cvv/doc/cvv_api.rst

-CVV : the API
-*************
-
-.. highlight:: cpp
-
-
-Introduction
-++++++++++++
-
-Namespace for all functions is **cvv**, i.e. *cvv::showImage()*.
-
-Compilation:
-
-* For development, i.e. for cvv GUI to show up, compile your code using cvv with *g++ -DCVVISUAL_DEBUGMODE*.
-* For release, i.e. cvv calls doing nothing, compile your code without above flag. 
-
-See cvv tutorial for a commented example application using cvv.
-
-
-
-
-API Functions
-+++++++++++++
-
-
-showImage
----------
-Add a single image to debug GUI (similar to :imshow:`imshow <>`).
-
-.. ocv:function:: void showImage(InputArray img, const CallMetaData& metaData, const string& description, const string& view)
-
-    :param img: Image to show in debug GUI.
-    :param metaData: Properly initialized CallMetaData struct, i.e. information about file, line and function name for GUI. Use CVVISUAL_LOCATION macro.
-    :param description: Human readable description to provide context to image.
-    :param view: Preselect view that will be used to visualize this image in GUI. Other views can still be selected in GUI later on.
-
-
-
-debugFilter
------------
-Add two images to debug GUI for comparison. Usually the input and output of some filter operation, whose result should be inspected.
-
-.. ocv:function:: void debugFilter(InputArray original, InputArray result, const CallMetaData& metaData, const string& description, const string& view)
-
-    :param original: First image for comparison, e.g. filter input.
-    :param result: Second image for comparison, e.g. filter output.
-    :param metaData: See :ocv:func:`showImage`
-    :param description: See :ocv:func:`showImage`
-    :param view: See :ocv:func:`showImage`
-
-
-
-debugDMatch
------------
-Add a filled in :basicstructures:`DMatch <dmatch>` to debug GUI. The matches can are visualized for interactive inspection in different GUI views (one similar to an interactive :draw_matches:`drawMatches<>`).
-
-.. ocv:function:: void debugDMatch(InputArray img1, std::vector<cv::KeyPoint> keypoints1, InputArray img2, std::vector<cv::KeyPoint> keypoints2, std::vector<cv::DMatch> matches, const CallMetaData& metaData, const string& description, const string& view, bool useTrainDescriptor)
-
-    :param img1: First image used in :basicstructures:`DMatch <dmatch>`.
-    :param keypoints1: Keypoints of first image.
-    :param img2:  Second image used in DMatch.
-    :param keypoints2:  Keypoints of second image.
-    :param metaData: See :ocv:func:`showImage`
-    :param description: See :ocv:func:`showImage`
-    :param view: See :ocv:func:`showImage`
-    :param useTrainDescriptor: Use :basicstructures:`DMatch <dmatch>`'s train descriptor index instead of query descriptor index.
-
-
-
-finalShow
----------
-This function **must** be called *once* *after* all cvv calls if any.
-As an alternative create an instance of FinalShowCaller, which calls finalShow() in its destructor (RAII-style).
-
-.. ocv:function:: void finalShow()
-
-
-
-setDebugFlag
-------------
-Enable or disable cvv for current translation unit and thread (disabled this way has higher - but still low - overhead compared to using the compile flags).
-
-.. ocv:function:: void setDebugFlag(bool active)
-
-    :param active: See above

modules/cvv/doc/cvv_gui.rst

-CVV : the GUI
-*************
-
-.. highlight:: cpp
-
-
-Introduction
-++++++++++++
-
-For now: See cvv tutorial.
-
-
-
-Overview
-++++++++
-
-
-Filter
-------
-
-
-
-Views
-++++++++

modules/cvv/tutorials/visual_debugging_introduction.markdown

+Interactive Visual Debugging of Computer Vision applications {#tutorial_cvv_introduction}
+============================================================
+
+What is the most common way to debug computer vision applications? Usually the answer is temporary,
+hacked together, custom code that must be removed from the code for release compilation.
+
+In this tutorial we will show how to use the visual debugging features of the **cvv** module
+(*opencv2/cvv.hpp*) instead.
+
+Goals
+-----
+
+In this tutorial you will learn how to:
+
+-   Add cvv debug calls to your application
+-   Use the visual debug GUI
+-   Enable and disable the visual debug features during compilation (with zero runtime overhead when
+    disabled)
+
+Code
+----
+
+The example code
+
+-   captures images (*videoio*), e.g. from a webcam,
+-   applies some filters to each image (*imgproc*),
+-   detects image features and matches them to the previous image (*features2d*).
+
+If the program is compiled without visual debugging (see CMakeLists.txt below) the only result is
+some information printed to the command line. We want to demonstrate how much debugging or
+development functionality is added by just a few lines of *cvv* commands.
+
+@includelineno cvv/samples/cvv_demo.cpp
+
+@code{.cmake}
+cmake_minimum_required(VERSION 2.8)
+
+project(cvvisual_test)
+
+SET(CMAKE_PREFIX_PATH ~/software/opencv/install)
+
+SET(CMAKE_CXX_COMPILER "g++-4.8")
+SET(CMAKE_CXX_FLAGS "-std=c++11 -O2 -pthread -Wall -Werror")
+
+# (un)set: cmake -DCVV_DEBUG_MODE=OFF ..
+OPTION(CVV_DEBUG_MODE "cvvisual-debug-mode" ON)
+if(CVV_DEBUG_MODE MATCHES ON)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCVVISUAL_DEBUGMODE")
+endif()
+
+
+FIND_PACKAGE(OpenCV REQUIRED)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+add_executable(cvvt main.cpp)
+target_link_libraries(cvvt
+  opencv_core opencv_videoio opencv_imgproc opencv_features2d
+  opencv_cvv
+)
+@endcode
+
+Explanation
+-----------
+
+-#  We compile the program either using the above CmakeLists.txt with Option *CVV_DEBUG_MODE=ON*
+    (*cmake -DCVV_DEBUG_MODE=ON*) or by adding the corresponding define *CVVISUAL_DEBUGMODE* to
+    our compiler (e.g. *g++ -DCVVISUAL_DEBUGMODE*).
+-#  The first cvv call simply shows the image (similar to *imshow*) with the imgIdString as comment.
+    @code{.cpp}
+    cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str());
+    @endcode
+    The image is added to the overview tab in the visual debug GUI and the cvv call blocks.
+
+    ![image](images/01_overview_single.jpg)
+
+    The image can then be selected and viewed
+
+    ![image](images/02_single_image_view.jpg)
+
+    Whenever you want to continue in the code, i.e. unblock the cvv call, you can either continue
+    until the next cvv call (*Step*), continue until the last cvv call (*\>\>*) or run the
+    application until it exists (*Close*).
+
+    We decide to press the green *Step* button.
+
+-#  The next cvv calls are used to debug all kinds of filter operations, i.e. operations that take a
+    picture as input and return a picture as output.
+    @code{.cpp}
+    cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray");
+    @endcode
+    As with every cvv call, you first end up in the overview.
+
+    ![image](images/03_overview_two.jpg)
+
+    We decide not to care about the conversion to gray scale and press *Step*.
+    @code{.cpp}
+    cvv::debugFilter(imgGray, imgGraySmooth, CVVISUAL_LOCATION, "smoothed");
+    @endcode
+    If you open the filter call, you will end up in the so called "DefaultFilterView". Both images
+    are shown next to each other and you can (synchronized) zoom into them.
+
+    ![image](images/04_default_filter_view.jpg)
+
+    When you go to very high zoom levels, each pixel is annotated with its numeric values.
+
+    ![image](images/05_default_filter_view_high_zoom.jpg)
+
+    We press *Step* twice and have a look at the dilated image.
+    @code{.cpp}
+    cvv::debugFilter(imgEdges, imgEdgesDilated, CVVISUAL_LOCATION, "dilated edges");
+    @endcode
+    The DefaultFilterView showing both images
+
+    ![image](images/06_default_filter_view_edges.jpg)
+
+    Now we use the *View* selector in the top right and select the "DualFilterView". We select
+    "Changed Pixels" as filter and apply it (middle image).
+
+    ![image](images/07_dual_filter_view_edges.jpg)
+
+    After we had a close look at these images, perhaps using different views, filters or other GUI
+    features, we decide to let the program run through. Therefore we press the yellow *\>\>* button.
+
+    The program will block at
+    @code{.cpp}
+    cvv::finalShow();
+    @endcode
+    and display the overview with everything that was passed to cvv in the meantime.
+
+    ![image](images/08_overview_all.jpg)
+
+-#  The cvv debugDMatch call is used in a situation where there are two images each with a set of
+    descriptors that are matched to each other.
+
+    We pass both images, both sets of keypoints and their matching to the visual debug module.
+    @code{.cpp}
+    cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str());
+    @endcode
+    Since we want to have a look at matches, we use the filter capabilities (*\#type match*) in the
+    overview to only show match calls.
+
+    ![image](images/09_overview_filtered_type_match.jpg)
+
+    We want to have a closer look at one of them, e.g. to tune our parameters that use the matching.
+    The view has various settings how to display keypoints and matches. Furthermore, there is a
+    mouseover tooltip.
+
+    ![image](images/10_line_match_view.jpg)
+
+    We see (visual debugging!) that there are many bad matches. We decide that only 70% of the
+    matches should be shown - those 70% with the lowest match distance.
+
+    ![image](images/11_line_match_view_portion_selector.jpg)
+
+    Having successfully reduced the visual distraction, we want to see more clearly what changed
+    between the two images. We select the "TranslationMatchView" that shows to where the keypoint
+    was matched in a different way.
+
+    ![image](images/12_translation_match_view_portion_selector.jpg)
+
+    It is easy to see that the cup was moved to the left during the two images.
+
+    Although, cvv is all about interactively *seeing* the computer vision bugs, this is complemented
+    by a "RawView" that allows to have a look at the underlying numeric data.
+
+    ![image](images/13_raw_view.jpg)
+
+-#  There are many more useful features contained in the cvv GUI. For instance, one can group the
+    overview tab.
+
+    ![image](images/14_overview_group_by_line.jpg)
+
+Result
+------
+
+-   By adding a view expressive lines to our computer vision program we can interactively debug it
+    through different visualizations.
+-   Once we are done developing/debugging we do not have to remove those lines. We simply disable
+    cvv debugging (*cmake -DCVV_DEBUG_MODE=OFF* or g++ without *-DCVVISUAL_DEBUGMODE*) and our
+    programs runs without any debug overhead.
+
+Enjoy computer vision!

modules/datasets/doc/ar_hmdb.rst

-HMDB: A Large Human Motion Database
-===================================
-.. ocv:class:: AR_hmdb
-
-Implements loading dataset:
-
-_`"HMDB: A Large Human Motion Database"`: http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
-
-.. note:: Usage
-
- 1. From link above download dataset files: hmdb51_org.rar & test_train_splits.rar.
-
- 2. Unpack them. Unpack all archives from directory: hmdb51_org/ and remove them.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_ar_hmdb -p=/home/user/path_to_unpacked_folders/
-
-Benchmark
-"""""""""
-
-For this dataset was implemented benchmark with accuracy: 0.107407 (using precomputed HOG/HOF "STIP" features from site, averaging for 3 splits)
-
-To run this benchmark execute:
-
-.. code-block:: bash
-
- ./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/
-
-(precomputed features should be unpacked in the same folder: /home/user/path_to_unpacked_folders/hmdb51_org_stips/. Also unpack all archives from directory: hmdb51_org_stips/ and remove them.)
-
-**References:**
-
-.. [Kuehne11] H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A Large Video Database for Human Motion Recognition. ICCV, 2011
-
-.. [Laptev08] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning Realistic Human Actions From Movies. CVPR, 2008
-
-

modules/datasets/doc/ar_sports.rst

-Sports-1M Dataset
-=================
-.. ocv:class:: AR_sports
-
-Implements loading dataset:
-
-_`"Sports-1M Dataset"`: http://cs.stanford.edu/people/karpathy/deepvideo/
-
-.. note:: Usage
-
- 1. From link above download dataset files (git clone https://code.google.com/p/sports-1m-dataset/).
-
- 2. To load data run: ./opencv/build/bin/example_datasets_ar_sports -p=/home/user/path_to_downloaded_folders/
-
-**References:**
-
-.. [KarpathyCVPR14] Andrej Karpathy and George Toderici and Sanketh Shetty and Thomas Leung and Rahul Sukthankar and Li Fei-Fei. Large-scale Video Classification with Convolutional Neural Networks. CVPR, 2014
-

modules/datasets/doc/datasets.rst

-*******************************************************
-datasets. Framework for working with different datasets
-*******************************************************
-
-.. highlight:: cpp
-
-The datasets module includes classes for working with different datasets: load data, evaluate different algorithms on them, contains benchmarks, etc.
-
-It is planned to have:
-
- * basic: loading code for all datasets to help start work with them.
- * next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and implement evaluation code.
- * finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks.
-
-.. toctree::
-    :hidden:
-
-    ar_hmdb
-    ar_sports
-    fr_adience
-    fr_lfw
-    gr_chalearn
-    gr_skig
-    hpe_humaneva
-    hpe_parse
-    ir_affine
-    ir_robot
-    is_bsds
-    is_weizmann
-    msm_epfl
-    msm_middlebury
-    or_imagenet
-    or_mnist
-    or_sun
-    pd_caltech
-    slam_kitti
-    slam_tumindoor
-    tr_chars
-    tr_svt
-
-Action Recognition
-------------------
-
-    :doc:`ar_hmdb` [#f1]_
-
-    :doc:`ar_sports`
-
-Face Recognition
-----------------
-
-    :doc:`fr_adience`
-
-    :doc:`fr_lfw` [#f1]_
-
-Gesture Recognition
--------------------
-
-    :doc:`gr_chalearn`
-
-    :doc:`gr_skig`
-
-Human Pose Estimation
----------------------
-
-    :doc:`hpe_humaneva`
-
-    :doc:`hpe_parse`
-
-Image Registration
-------------------
-
-    :doc:`ir_affine`
-
-    :doc:`ir_robot`
-
-Image Segmentation
-------------------
-
-    :doc:`is_bsds`
-
-    :doc:`is_weizmann`
-
-Multiview Stereo Matching
--------------------------
-
-    :doc:`msm_epfl`
-
-    :doc:`msm_middlebury`
-
-Object Recognition
-------------------
-
-    :doc:`or_imagenet`
-
-    :doc:`or_mnist` [#f2]_
-
-    :doc:`or_sun`
-
-Pedestrian Detection
---------------------
-
-    :doc:`pd_caltech` [#f2]_
-
-SLAM
-----
-
-    :doc:`slam_kitti`
-
-    :doc:`slam_tumindoor`
-
-Text Recognition
-----------------
-
-    :doc:`tr_chars`
-
-    :doc:`tr_svt` [#f1]_
-
-*Footnotes*
-
- .. [#f1] Benchmark implemented
- .. [#f2] Not used in Vision Challenge

modules/datasets/doc/fr_adience.rst

-Adience
-=======
-.. ocv:class:: FR_adience
-
-Implements loading dataset:
-
-_`"Adience"`: http://www.openu.ac.il/home/hassner/Adience/data.html
-
-.. note:: Usage
-
- 1. From link above download any dataset file: faces.tar.gz\\aligned.tar.gz and files with splits: fold_0_data.txt-fold_4_data.txt, fold_frontal_0_data.txt-fold_frontal_4_data.txt. (For face recognition task another splits should be created)
-
- 2. Unpack dataset file to some folder and place split files into the same folder.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/
-
-**References:**
-
-.. [Eidinger] E. Eidinger, R. Enbar, and T. Hassner. Age and Gender Estimation of Unfiltered Faces
-

modules/datasets/doc/fr_lfw.rst

-Labeled Faces in the Wild
-=========================
-.. ocv:class:: FR_lfw
-
-Implements loading dataset:
-
-_`"Labeled Faces in the Wild"`: http://vis-www.cs.umass.edu/lfw/
-
-.. note:: Usage
-
- 1. From link above download any dataset file: lfw.tgz\\lfwa.tar.gz\\lfw-deepfunneled.tgz\\lfw-funneled.tgz and files with pairs: 10 test splits: pairs.txt and developer train split: pairsDevTrain.txt.
-
- 2. Unpack dataset file and place pairs.txt and pairsDevTrain.txt in created folder.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/
-
-Benchmark
-"""""""""
-
-For this dataset was implemented benchmark with accuracy: 0.623833 +- 0.005223 (train split: pairsDevTrain.txt, dataset: lfwa)
-
-To run this benchmark execute:
-
-.. code-block:: bash
-
- ./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/
-
-**References:**
-
-.. [Huang07] G.B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. 2007
-

modules/datasets/doc/gr_chalearn.rst

-ChaLearn Looking at People
-==========================
-.. ocv:class:: GR_chalearn
-
-Implements loading dataset:
-
-_`"ChaLearn Looking at People"`: http://gesture.chalearn.org/
-
-.. note:: Usage
-
- 1. Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site: www.codalab.org and accept the terms and conditions of competition: https://www.codalab.org/competitions/991#learn_the_details There are three mirrors for downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" works).
-
- 2. Unpack train archives Train1.zip-Train5.zip to folder Train/, validation archives Validation1.zip-Validation3.zip to folder Validation/
-
- 3. Unpack all archives in Train/ & Validation/ in the folders with the same names, for example: Sample0001.zip to Sample0001/
-
- 4. To load data run: ./opencv/build/bin/example_datasets_gr_chalearn -p=/home/user/path_to_unpacked_folders/
-
-**References:**
-
-.. [Escalera14] S. Escalera, X. Baró, J. Gonzàlez, M.A. Bautista, M. Madadi, M. Reyes, V. Ponce-López, H.J. Escalante, J. Shotton, I. Guyon, "ChaLearn Looking at People Challenge 2014: Dataset and Results", ECCV Workshops, 2014
-

modules/datasets/doc/gr_skig.rst

-Sheffield Kinect Gesture Dataset
-================================
-.. ocv:class:: GR_skig
-
-Implements loading dataset:
-
-_`"Sheffield Kinect Gesture Dataset"`: http://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm
-
-.. note:: Usage
-
- 1. From link above download dataset files: subject1_dep.7z-subject6_dep.7z, subject1_rgb.7z-subject6_rgb.7z.
-
- 2. Unpack them.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_gr_skig -p=/home/user/path_to_unpacked_folders/
-
-**References:**
-
-.. [Liu13] L. Liu and L. Shao, “Learning Discriminative Representations from RGB-D Video Data”, In Proc. International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013.
-

modules/datasets/doc/hpe_humaneva.rst

-HumanEva Dataset
-================
-.. ocv:class:: HPE_humaneva
-
-Implements loading dataset:
-
-_`"HumanEva Dataset"`: http://humaneva.is.tue.mpg.de
-
-.. note:: Usage
-
- 1. From link above download dataset files for HumanEva-I (tar) & HumanEva-II.
-
- 2. Unpack them to HumanEva_1 & HumanEva_2 accordingly.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_hpe_humaneva -p=/home/user/path_to_unpacked_folders/
-
-**References:**
-
-.. [Sigal10] L. Sigal, A. Balan and M. J. Black. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion, In International Journal of Computer Vision, Vol. 87 (1-2), 2010
-
-.. [Sigal06] L. Sigal and M. J. Black. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion, Techniacl Report CS-06-08, Brown University, 2006
-

modules/datasets/doc/hpe_parse.rst

-PARSE Dataset
-=============
-.. ocv:class:: HPE_parse
-
-Implements loading dataset:
-
-_`"PARSE Dataset"`: http://www.ics.uci.edu/~dramanan/papers/parse/
-
-.. note:: Usage
-
- 1. From link above download dataset file: people.zip.
-
- 2. Unpack it.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/
-
-**References:**
-
-.. [Ramanan06] D. Ramanan "Learning to Parse Images of Articulated Bodies." Neural Info. Proc. Systems (NIPS) To appear. Dec 2006.
-

modules/datasets/doc/ir_affine.rst

-Affine Covariant Regions Datasets
-=================================
-.. ocv:class:: IR_affine
-
-Implements loading dataset:
-
-_`"Affine Covariant Regions Datasets"`: http://www.robots.ox.ac.uk/~vgg/data/data-aff.html
-
-.. note:: Usage
-
- 1. From link above download dataset files: bark\\bikes\\boat\\graf\\leuven\\trees\\ubc\\wall.tar.gz.
-
- 2. Unpack them.
-
- 3. To load data, for example, for "bark", run: ./opencv/build/bin/example_datasets_ir_affine -p=/home/user/path_to_unpacked_folder/bark/
-
-**References:**
-
-.. [Mikolajczyk05]  K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool. A Comparison of Affine Region Detectors. International Journal of Computer Vision, Volume 65, Number 1/2, page 43--72, 2005
-

modules/datasets/doc/ir_robot.rst

-Robot Data Set
-==============
-.. ocv:class:: IR_robot
-
-Implements loading dataset:
-
-_`"Robot Data Set, Point Feature Data Set – 2010"`: http://roboimagedata.compute.dtu.dk/?page_id=24
-
-.. note:: Usage
-
- 1. From link above download dataset files: SET001_6.tar.gz-SET055_60.tar.gz
-
- 2. Unpack them to one folder.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_ir_robot -p=/home/user/path_to_unpacked_folder/
-
-**References:**
-
-.. [aanæsinteresting] Aan{\ae}s, H. and Dahl, A.L. and Steenstrup Pedersen, K. Interesting Interest Points. International Journal of Computer Vision. 2012.
-

modules/datasets/doc/is_bsds.rst

-The Berkeley Segmentation Dataset and Benchmark
-===============================================
-.. ocv:class:: IS_bsds
-
-Implements loading dataset:
-
-_`"The Berkeley Segmentation Dataset and Benchmark"`: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
-
-.. note:: Usage
-
- 1. From link above download dataset files: BSDS300-human.tgz & BSDS300-images.tgz.
-
- 2. Unpack them.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/
-
-**References:**
-
-.. [MartinFTM01] D. Martin and C. Fowlkes and D. Tal and J. Malik. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. 2001
-

modules/datasets/doc/is_weizmann.rst

-Weizmann Segmentation Evaluation Database
-=========================================
-.. ocv:class:: IS_weizmann
-
-Implements loading dataset:
-
-_`"Weizmann Segmentation Evaluation Database"`: http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/
-
-.. note:: Usage
-
- 1. From link above download dataset files: Weizmann_Seg_DB_1obj.ZIP & Weizmann_Seg_DB_2obj.ZIP.
-
- 2. Unpack them.
-
- 3. To load data, for example, for 1 object dataset, run: ./opencv/build/bin/example_datasets_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/
-
-**References:**
-
-.. [AlpertGBB07] Sharon Alpert and Meirav Galun and Ronen Basri and Achi Brandt. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. 2007
-

modules/datasets/doc/msm_epfl.rst

-EPFL Multi-View Stereo
-======================
-.. ocv:class:: MSM_epfl
-
-Implements loading dataset:
-
-_`"EPFL Multi-View Stereo"`: http://cvlabwww.epfl.ch/~strecha/multiview/denseMVS.html
-
-.. note:: Usage
-
- 1. From link above download dataset files: castle_dense\\castle_dense_large\\castle_entry\\fountain\\herzjesu_dense\\herzjesu_dense_large_bounding\\cameras\\images\\p.tar.gz.
-
- 2. Unpack them in separate folder for each object. For example, for "fountain", in folder fountain/ : fountain_dense_bounding.tar.gz -> bounding/, fountain_dense_cameras.tar.gz -> camera/, fountain_dense_images.tar.gz -> png/, fountain_dense_p.tar.gz -> P/
-
- 3. To load data, for example, for "fountain", run: ./opencv/build/bin/example_datasets_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/
-
-**References:**
-
-.. [Strecha08] C. Strecha, W. von Hansen, L. Van Gool, P. Fua, U. Thoennessen. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. CVPR, 2008
-

modules/datasets/doc/msm_middlebury.rst

-Stereo – Middlebury Computer Vision
-===================================
-.. ocv:class:: MSM_middlebury
-
-Implements loading dataset:
-
-_`"Stereo – Middlebury Computer Vision"`: http://vision.middlebury.edu/mview/
-
-.. note:: Usage
-
- 1. From link above download dataset files: dino\\dinoRing\\dinoSparseRing\\temple\\templeRing\\templeSparseRing.zip
-
- 2. Unpack them.
-
- 3. To load data, for example "temple" dataset, run: ./opencv/build/bin/example_datasets_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/
-
-**References:**
-
-.. [Seitz06] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR, 2006
-

modules/datasets/doc/or_imagenet.rst

-ImageNet
-========
-.. ocv:class:: OR_imagenet
-
-Implements loading dataset:
-
-_`"ImageNet"`: http://www.image-net.org/
-
-.. note:: Usage
-
- 1. From link above download dataset files: ILSVRC2010_images_train.tar\\ILSVRC2010_images_test.tar\\ILSVRC2010_images_val.tar & devkit: ILSVRC2010_devkit-1.0.tar.gz (Implemented loading of 2010 dataset as only this dataset has ground truth for test data, but structure for ILSVRC2014 is similar)
-
- 2. Unpack them to: some_folder/train/\\some_folder/test/\\some_folder/val & some_folder/ILSVRC2010_validation_ground_truth.txt\\some_folder/ILSVRC2010_test_ground_truth.txt.
-
- 3. Create file with labels: some_folder/labels.txt, for example, using :ref:`python script <python-script>` below (each file's row format: synset,labelID,description. For example: "n07751451,18,plum").
-
- 4. Unpack all tar files in train.
-
- 5. To load data run: ./opencv/build/bin/example_datasets_or_imagenet -p=/home/user/some_folder/
-
-.. _python-script:
-
-Python script to parse meta.mat:
-
-::
-
- import scipy.io
- meta_mat = scipy.io.loadmat("devkit-1.0/data/meta.mat")
-
- labels_dic = dict((m[0][1][0], m[0][0][0][0]-1) for m in meta_mat['synsets']
- label_names_dic = dict((m[0][1][0], m[0][2][0]) for m in meta_mat['synsets']
-
- for label in labels_dic.keys():
-     print "{0},{1},{2}".format(label, labels_dic[label], label_names_dic[label])
-
-**References:**
-
-.. [ILSVRCarxiv14] Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. 2014
-

modules/datasets/doc/or_mnist.rst

-MNIST
-=====
-.. ocv:class:: OR_mnist
-
-Implements loading dataset:
-
-_`"MNIST"`: http://yann.lecun.com/exdb/mnist/
-
-.. note:: Usage
-
- 1. From link above download dataset files: t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz.
-
- 2. Unpack them.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_or_mnist -p=/home/user/path_to_unpacked_files/
-
-**References:**
-
-.. [LeCun98a] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
-

modules/datasets/doc/or_sun.rst

-SUN Database
-============
-.. ocv:class:: OR_sun
-
-Implements loading dataset:
-
-_`"SUN Database, Scene Recognition Benchmark. SUN397"`: http://vision.cs.princeton.edu/projects/2010/SUN/
-
-.. note:: Usage
-
- 1. From link above download dataset file: SUN397.tar & file with splits: Partitions.zip
-
- 2. Unpack SUN397.tar into folder: SUN397/ & Partitions.zip into folder: SUN397/Partitions/
-
- 3. To load data run: ./opencv/build/bin/example_datasets_or_sun -p=/home/user/path_to_unpacked_files/SUN397/
-
-**References:**
-
-.. [Xiao10] J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN Database: Large-scale Scene Recognition from Abbey to Zoo. IEEE Conference on Computer Vision and Pattern Recognition. CVPR, 2010
-
-.. [Xiao14] J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva. SUN Database: Exploring a Large Collection of Scene Categories. International Journal of Computer Vision. IJCV, 2014
-

modules/datasets/doc/pd_caltech.rst

-Caltech Pedestrian Detection Benchmark
-======================================
-.. ocv:class:: PD_caltech
-
-Implements loading dataset:
-
-_`"Caltech Pedestrian Detection Benchmark"`: http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
-
-.. note:: First version of Caltech Pedestrian dataset loading.
-
- Code to unpack all frames from seq files commented as their number is huge!
- So currently load only meta information without data.
-
- Also ground truth isn't processed, as need to convert it from mat files first.
-
-.. note:: Usage
-
- 1. From link above download dataset files: set00.tar-set10.tar.
-
- 2. Unpack them to separate folder.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_pd_caltech -p=/home/user/path_to_unpacked_folders/
-
-**References:**
-
-.. [Dollár12] P. Dollár, C. Wojek, B. Schiele and P. Perona. Pedestrian Detection: An Evaluation of the State of the Art. PAMI, 2012.
-
-.. [DollárCVPR09] P. Dollár, C. Wojek, B. Schiele and P. Perona. Pedestrian Detection: A Benchmark. CVPR, 2009
-

modules/datasets/doc/slam_kitti.rst

-KITTI Vision Benchmark
-======================
-.. ocv:class:: SLAM_kitti
-
-Implements loading dataset:
-
-_`"KITTI Vision Benchmark"`: http://www.cvlibs.net/datasets/kitti/eval_odometry.php
-
-.. note:: Usage
-
- 1. From link above download "Odometry" dataset files: data_odometry_gray\\data_odometry_color\\data_odometry_velodyne\\data_odometry_poses\\data_odometry_calib.zip.
-
- 2. Unpack data_odometry_poses.zip, it creates folder dataset/poses/. After that unpack data_odometry_gray.zip, data_odometry_color.zip, data_odometry_velodyne.zip. Folder dataset/sequences/ will be created with folders 00/..21/. Each of these folders will contain: image_0/, image_1/, image_2/, image_3/, velodyne/ and files calib.txt & times.txt. These two last files will be replaced after unpacking data_odometry_calib.zip at the end.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/
-
-**References:**
-
-.. [Geiger2012CVPR] Andreas Geiger and Philip Lenz and Raquel Urtasun. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. CVPR, 2012
-
-.. [Geiger2013IJRR] Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun. Vision meets Robotics: The KITTI Dataset. IJRR, 2013
-
-.. [Fritsch2013ITSC] Jannik Fritsch and Tobias Kuehnl and Andreas Geiger. A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. ITSC, 2013
-

modules/datasets/doc/slam_tumindoor.rst

-TUMindoor Dataset
-=================
-.. ocv:class:: SLAM_tumindoor
-
-Implements loading dataset:
-
-_`"TUMindoor Dataset"`: http://www.navvis.lmt.ei.tum.de/dataset/
-
-.. note:: Usage
-
- 1. From link above download dataset files: dslr\\info\\ladybug\\pointcloud.tar.bz2 for each dataset: 11-11-28 (1st floor)\\11-12-13 (1st floor N1)\\11-12-17a (4th floor)\\11-12-17b (3rd floor)\\11-12-17c (Ground I)\\11-12-18a (Ground II)\\11-12-18b (2nd floor)
-
- 2. Unpack them in separate folder for each dataset. dslr.tar.bz2 -> dslr/, info.tar.bz2 -> info/, ladybug.tar.bz2 -> ladybug/, pointcloud.tar.bz2 -> pointcloud/.
-
- 3. To load each dataset run: ./opencv/build/bin/example_datasets_slam_tumindoor -p=/home/user/path_to_unpacked_folders/
-
-**References:**
-
-.. [TUMindoor] R. Huitl and G. Schroth and S. Hilsenbeck and F. Schweiger and E. Steinbach. {TUM}indoor: An Extensive Image and Point Cloud Dataset for Visual Indoor Localization and Mapping. 2012
-

modules/datasets/doc/tr_chars.rst

-The Chars74K Dataset
-====================
-.. ocv:class:: TR_chars
-
-Implements loading dataset:
-
-_`"The Chars74K Dataset"`: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/
-
-.. note:: Usage
-
- 1. From link above download dataset files: EnglishFnt\\EnglishHnd\\EnglishImg\\KannadaHnd\\KannadaImg.tgz, ListsTXT.tgz.
-
- 2. Unpack them.
-
- 3. Move .m files from folder ListsTXT/ to appropriate folder. For example, English/list_English_Img.m for EnglishImg.tgz.
-
- 4. To load data, for example "EnglishImg", run: ./opencv/build/bin/example_datasets_tr_chars -p=/home/user/path_to_unpacked_folder/English/
-
-**References:**
-
-.. [Campos09] T. E. de Campos, B. R. Babu and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), 2009
-

modules/datasets/doc/tr_svt.rst

-The Street View Text Dataset
-============================
-.. ocv:class:: TR_svt
-
-Implements loading dataset:
-
-_`"The Street View Text Dataset"`: http://vision.ucsd.edu/~kai/svt/
-
-.. note:: Usage
-
- 1. From link above download dataset file: svt.zip.
-
- 2. Unpack it.
-
- 3. To load data run: ./opencv/build/bin/example_datasets_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/
-
-Benchmark
-"""""""""
-
-For this dataset was implemented benchmark with accuracy (mean f1): 0.217
-
-To run benchmark execute:
-
-.. code-block:: bash
-
- ./opencv/build/bin/example_datasets_tr_svt_benchmark -p=/home/user/path_to_unpacked_folders/svt/svt1/
-
-**References:**
-
-.. [Wang11] Kai Wang, Boris Babenko and Serge Belongie. End-to-end Scene Text Recognition. ICCV, 2011
-
-.. [Wang10] Kai Wang and Serge Belongie. Word Spotting in the Wild. ECCV, 2010
-

modules/datasets/include/opencv2/datasets/dataset.hpp

@@ -282,7 +282,7 @@ Usage:
 
 Implements loading dataset:
 
-"EPFL Multi-View Stereo": <http://cvlabwww.epfl.ch/~strecha/multiview/denseMVS.html>
+"EPFL Multi-View Stereo": <http://cvlab.epfl.ch/data/strechamvs>
 
 Usage:
 -# From link above download dataset files:

modules/datasets/samples/fr_lfw_benchmark.cpp

@@ -78,7 +78,9 @@ int main(int argc, const char *argv[])
 {
     const char *keys =
             "{ help h usage ? |    | show this message }"
-            "{ path p         |true| path to dataset (lfw2 folder) }";
+            "{ path p         |true| path to dataset (lfw2 folder) }"
+            "{ train t        |dev | train method: 'dev'(pairsDevTrain.txt) or 'split'(pairs.txt) }";
+
     CommandLineParser parser(argc, argv, keys);
     string path(parser.get<string>("path"));
     if (parser.has("help") || path=="true")
@@ -86,6 +88,7 @@ int main(int argc, const char *argv[])
         parser.printMessage();
         return -1;
     }
+    string trainMethod(parser.get<string>("train"));
 
     // These vectors hold the images and corresponding labels.
     vector<Mat> images;
@@ -97,24 +100,12 @@ int main(int argc, const char *argv[])
 
     unsigned int numSplits = dataset->getNumSplits();
     printf("splits number: %u\n", numSplits);
-    printf("train size: %u\n", (unsigned int)dataset->getTrain().size());
+    if (trainMethod == "dev")
+        printf("train size: %u\n", (unsigned int)dataset->getTrain().size());
+    else
+        printf("train size: %u\n", (numSplits-1) * (unsigned int)dataset->getTest().size());
     printf("test size: %u\n", (unsigned int)dataset->getTest().size());
 
-    for (unsigned int i=0; i<dataset->getTrain().size(); ++i)
-    {
-        FR_lfwObj *example = static_cast<FR_lfwObj *>(dataset->getTrain()[i].get());
-
-        int currNum = getLabel(example->image1);
-        Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
-        images.push_back(img);
-        labels.push_back(currNum);
-
-        currNum = getLabel(example->image2);
-        img = imread(path+example->image2, IMREAD_GRAYSCALE);
-        images.push_back(img);
-        labels.push_back(currNum);
-    }
-
     // 2200 pairsDevTrain, first split: correct: 373, from: 600 -> 62.1667%
     Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
     // 2200 pairsDevTrain, first split: correct: correct: 369, from: 600 -> 61.5%
@@ -122,14 +113,58 @@ int main(int argc, const char *argv[])
     // 2200 pairsDevTrain, first split: correct: 372, from: 600 -> 62%
     //Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
 
-    model->train(images, labels);
-    //string saveModelPath = "face-rec-model.txt";
-    //cout << "Saving the trained model to " << saveModelPath << endl;
-    //model->save(saveModelPath);
+    if (trainMethod == "dev") // train on personsDevTrain.txt
+    {
+        for (unsigned int i=0; i<dataset->getTrain().size(); ++i)
+        {
+            FR_lfwObj *example = static_cast<FR_lfwObj *>(dataset->getTrain()[i].get());
+
+            int currNum = getLabel(example->image1);
+            Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
+            images.push_back(img);
+            labels.push_back(currNum);
+
+            currNum = getLabel(example->image2);
+            img = imread(path+example->image2, IMREAD_GRAYSCALE);
+            images.push_back(img);
+            labels.push_back(currNum);
+        }
+        model->train(images, labels);
+        //string saveModelPath = "face-rec-model.txt";
+        //cout << "Saving the trained model to " << saveModelPath << endl;
+        //model->save(saveModelPath);
+    }
 
     vector<double> p;
     for (unsigned int j=0; j<numSplits; ++j)
     {
+        if (trainMethod == "split") // train on the remaining 9 splits from pairs.txt
+        {
+            images.clear();
+            labels.clear();
+            for (unsigned int j2=0; j2<numSplits; ++j2)
+            {
+                if (j==j2) continue; // skip test split for training
+
+                vector < Ptr<Object> > &curr = dataset->getTest(j2);
+                for (unsigned int i=0; i<curr.size(); ++i)
+                {
+                    FR_lfwObj *example = static_cast<FR_lfwObj *>(curr[i].get());
+
+                    int currNum = getLabel(example->image1);
+                    Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
+                    images.push_back(img);
+                    labels.push_back(currNum);
+
+                    currNum = getLabel(example->image2);
+                    img = imread(path+example->image2, IMREAD_GRAYSCALE);
+                    images.push_back(img);
+                    labels.push_back(currNum);
+                }
+            }
+            model->train(images, labels);
+        }
+
         unsigned int incorrect = 0, correct = 0;
         vector < Ptr<Object> > &curr = dataset->getTest(j);
         for (unsigned int i=0; i<curr.size(); ++i)
@@ -168,7 +203,7 @@ int main(int argc, const char *argv[])
         sigma += (*it - mu)*(*it - mu);
     }
     sigma = sqrt(sigma/p.size());
-    double se = sigma/sqrt(p.size());
+    double se = sigma/sqrt(double(p.size()));
     printf("estimated mean accuracy: %f and the standard error of the mean: %f\n", mu, se);
 
     return 0;

modules/face/doc/face.rst

-***************************************
-face. Face Recognition
-***************************************
-
-The module contains some recently added functionality that has not been stabilized, or functionality that is considered optional.
-
-.. toctree::
-    :maxdepth: 2
-
-    FaceRecognizer Documentation <index>

modules/face/doc/facerec_changelog.rst

-Changelog
-=========
-
-Release 0.05
-------------
-
-This library is now included in the official OpenCV distribution (from 2.4 on).
-The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall
-OpenCV API.
-
-To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
-have been synchronized and are now based on the same interfaces and implementation.
-
-The library now has an extensive documentation:
-
-* The API is explained in detail and with a lot of code examples.
-* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
-* A tutorial for gender classification with Fisherfaces.
-* A tutorial for face recognition in videos (e.g. webcam).
-
-
-Release highlights
-++++++++++++++++++
-
-* There are no single highlights to pick from, this release is a highlight itself.
-
-Release 0.04
-------------
-
-This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
-bugfixes, but none influenced the recognition rate.
-
-Release highlights
-++++++++++++++++++
-
-* A whole lot of exceptions with meaningful error messages.
-* A tutorial for Windows users: `http://bytefish.de/blog/opencv_visual_studio_and_libfacerec <http://bytefish.de/blog/opencv_visual_studio_and_libfacerec>`_
-
-
-Release 0.03
-------------
-
-Reworked the library to provide separate implementations in cpp files, because
-it's the preferred way of contributing OpenCV libraries. This means the library
-is not header-only anymore. Slight API changes were done, please see the
-documentation for details.
-
-Release highlights
-++++++++++++++++++
-
-* New Unit Tests (for LBP Histograms) make the library more robust.
-* Added more documentation.
-
-
-Release 0.02
-------------
-
-Reworked the library to provide separate implementations in cpp files, because
-it's the preferred way of contributing OpenCV libraries. This means the library
-is not header-only anymore. Slight API changes were done, please see the
-documentation for details.
-
-Release highlights
-++++++++++++++++++
-
-* New Unit Tests (for LBP Histograms) make the library more robust.
-* Added a documentation and changelog in reStructuredText.
-
-Release 0.01
-------------
-
-Initial release as header-only library.
-
-Release highlights
-++++++++++++++++++
-
-* Colormaps for OpenCV to enhance the visualization.
-* Face Recognition algorithms implemented:
-
-  * Eigenfaces [TP91]_
-  * Fisherfaces [BHK97]_
-  * Local Binary Patterns Histograms [AHP04]_
-
-* Added persistence facilities to store the models with a common API.
-* Unit Tests (using `gtest <http://code.google.com/p/googletest/>`_).
-* Providing a CMakeLists.txt to enable easy cross-platform building.

modules/face/doc/index.rst

-FaceRecognizer - Face Recognition with OpenCV
-##############################################
-
-OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition. This documentation is going to explain you :doc:`the API <facerec_api>` in detail and it will give you a lot of help to get started (full source code examples). :doc:`Face Recognition with OpenCV <facerec_tutorial>` is the definite guide to the new :ocv:class:`FaceRecognizer`. There's also a :doc:`tutorial on gender classification <tutorial/facerec_gender_classification>`, a :doc:`tutorial for face recognition in videos <tutorial/facerec_video_recognition>` and it's shown :doc:`how to load & save your results <tutorial/facerec_save_load>`.
-
-These documents are the help I have wished for, when I was working myself into face recognition. I hope you also think the new :ocv:class:`FaceRecognizer` is a useful addition to OpenCV.
-
-Please issue any feature requests and/or bugs on the official OpenCV bug tracker at:
-
-  * http://code.opencv.org/projects/opencv/issues
-
-Contents
-========
-
-
-.. toctree::
-   :maxdepth: 1
-
-   FaceRecognizer API <facerec_api>
-   Guide to Face Recognition with OpenCV <facerec_tutorial>
-   Tutorial on Gender Classification <tutorial/facerec_gender_classification>
-   Tutorial on Face Recognition in Videos <tutorial/facerec_video_recognition>
-   Tutorial On Saving & Loading a FaceRecognizer <tutorial/facerec_save_load>
-   Changelog <facerec_changelog>
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

modules/face/doc/tutorial/facerec_save_load.rst

-Saving and Loading a FaceRecognizer
-===================================
-
-Introduction
-------------
-
-Saving and loading a :ocv:class:`FaceRecognizer` is very important. Training a FaceRecognizer can be a very time-intense task, plus it's often impossible to ship the whole face database to the user of your product. The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRecognizer`. You only have to call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a :ocv:class:`FaceRecognizer`.
-
-I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_, store the model to a YAML file and then load it again.
-
-From the loaded model, we'll get a prediction, show the mean, Eigenfaces and the image reconstruction.
-
-Using FaceRecognizer::save and FaceRecognizer::load
------------------------------------------------------
-
-The source code for this demo application is also available in the ``src`` folder coming with this documentation:
-
-* :download:`src/facerec_save_load.cpp <../src/facerec_save_load.cpp>`
-
-.. literalinclude:: ../src/facerec_save_load.cpp
-   :language: cpp
-   :linenos:
-
-Results
--------
-
-``eigenfaces_at.yml`` then contains the model state, we'll simply look at the first 10 lines with ``head eigenfaces_at.yml``:
-
-.. code-block:: none
-
-  philipp@mango:~/github/libfacerec-build$ head eigenfaces_at.yml
-  %YAML:1.0
-  num_components: 399
-  mean: !!opencv-matrix
-     rows: 1
-     cols: 10304
-     dt: d
-     data: [ 8.5558897243107765e+01, 8.5511278195488714e+01,
-         8.5854636591478695e+01, 8.5796992481203006e+01,
-         8.5952380952380949e+01, 8.6162907268170414e+01,
-         8.6082706766917283e+01, 8.5776942355889716e+01,
-
-And here is the Reconstruction, which is the same as the original:
-
-.. image:: ../img/eigenface_reconstruction_opencv.png
-    :align: center

modules/face/include/opencv2/face.hpp

@@ -43,7 +43,7 @@ the use of this software, even if advised of the possibility of such damage.
 @defgroup face Face Recognition
 
 - @ref face_changelog
-- @ref face_tutorial
+- @ref tutorial_face_main
 
 */
 

modules/face/include/opencv2/face/facerec.hpp

@@ -20,13 +20,19 @@ namespace cv { namespace face {
 class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer
 {
 public:
-    CV_PURE_PROPERTY(int, NumComponents)
-    CV_PURE_PROPERTY(double, Threshold)
-    CV_PURE_PROPERTY_RO(std::vector<cv::Mat>, Projections)
-    CV_PURE_PROPERTY_RO(cv::Mat, Labels)
-    CV_PURE_PROPERTY_RO(cv::Mat, EigenValues)
-    CV_PURE_PROPERTY_RO(cv::Mat, EigenVectors)
-    CV_PURE_PROPERTY_RO(cv::Mat, Mean)
+    /** @see setNumComponents */
+    CV_WRAP virtual int getNumComponents() const = 0;
+    /** @copybrief getNumComponents @see getNumComponents */
+    CV_WRAP virtual void setNumComponents(int val) = 0;
+    /** @see setThreshold */
+    CV_WRAP virtual double getThreshold() const = 0;
+    /** @copybrief getThreshold @see getThreshold */
+    CV_WRAP virtual void setThreshold(double val) = 0;
+    CV_WRAP virtual std::vector<cv::Mat> getProjections() const = 0;
+    CV_WRAP virtual cv::Mat getLabels() const = 0;
+    CV_WRAP virtual cv::Mat getEigenValues() const = 0;
+    CV_WRAP virtual cv::Mat getEigenVectors() const = 0;
+    CV_WRAP virtual cv::Mat getMean() const = 0;
 };
 
 /**
@@ -95,13 +101,28 @@ CV_EXPORTS_W Ptr<BasicFaceRecognizer> createFisherFaceRecognizer(int num_compone
 class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer
 {
 public:
-    CV_PURE_PROPERTY(int, GridX)
-    CV_PURE_PROPERTY(int, GridY)
-    CV_PURE_PROPERTY(int, Radius)
-    CV_PURE_PROPERTY(int, Neighbors)
-    CV_PURE_PROPERTY(double, Threshold)
-    CV_PURE_PROPERTY_RO(std::vector<cv::Mat>, Histograms)
-    CV_PURE_PROPERTY_RO(cv::Mat, Labels)
+    /** @see setGridX */
+    CV_WRAP virtual int getGridX() const = 0;
+    /** @copybrief getGridX @see getGridX */
+    CV_WRAP virtual void setGridX(int val) = 0;
+    /** @see setGridY */
+    CV_WRAP virtual int getGridY() const = 0;
+    /** @copybrief getGridY @see getGridY */
+    CV_WRAP virtual void setGridY(int val) = 0;
+    /** @see setRadius */
+    CV_WRAP virtual int getRadius() const = 0;
+    /** @copybrief getRadius @see getRadius */
+    CV_WRAP virtual void setRadius(int val) = 0;
+    /** @see setNeighbors */
+    CV_WRAP virtual int getNeighbors() const = 0;
+    /** @copybrief getNeighbors @see getNeighbors */
+    CV_WRAP virtual void setNeighbors(int val) = 0;
+    /** @see setThreshold */
+    CV_WRAP virtual double getThreshold() const = 0;
+    /** @copybrief getThreshold @see getThreshold */
+    CV_WRAP virtual void setThreshold(double val) = 0;
+    CV_WRAP virtual std::vector<cv::Mat> getHistograms() const = 0;
+    CV_WRAP virtual cv::Mat getLabels() const = 0;
 };
 
 /**